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  <div class="section" id="numpy-random-random-integers">
<h1>numpy.random.random_integers<a class="headerlink" href="#numpy-random-random-integers" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.random.random_integers">
<code class="sig-prename descclassname">numpy.random.</code><code class="sig-name descname">random_integers</code><span class="sig-paren">(</span><em class="sig-param">low</em>, <em class="sig-param">high=None</em>, <em class="sig-param">size=None</em><span class="sig-paren">)</span><a class="headerlink" href="#numpy.random.random_integers" title="Permalink to this definition">¶</a></dt>
<dd><p>Random integers of type <em class="xref py py-obj">np.int_</em> between <em class="xref py py-obj">low</em> and <em class="xref py py-obj">high</em>, inclusive.</p>
<p>Return random integers of type <em class="xref py py-obj">np.int_</em> from the “discrete uniform”
distribution in the closed interval [<em class="xref py py-obj">low</em>, <em class="xref py py-obj">high</em>].  If <em class="xref py py-obj">high</em> is
None (the default), then results are from [1, <em class="xref py py-obj">low</em>]. The <em class="xref py py-obj">np.int_</em>
type translates to the C long integer type and its precision
is platform dependent.</p>
<p>This function has been deprecated. Use randint instead.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 1.11.0.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>low</strong><span class="classifier">int</span></dt><dd><p>Lowest (signed) integer to be drawn from the distribution (unless
<code class="docutils literal notranslate"><span class="pre">high=None</span></code>, in which case this parameter is the <em>highest</em> such
integer).</p>
</dd>
<dt><strong>high</strong><span class="classifier">int, optional</span></dt><dd><p>If provided, the largest (signed) integer to be drawn from the
distribution (see above for behavior if <code class="docutils literal notranslate"><span class="pre">high=None</span></code>).</p>
</dd>
<dt><strong>size</strong><span class="classifier">int or tuple of ints, optional</span></dt><dd><p>Output shape.  If the given shape is, e.g., <code class="docutils literal notranslate"><span class="pre">(m,</span> <span class="pre">n,</span> <span class="pre">k)</span></code>, then
<code class="docutils literal notranslate"><span class="pre">m</span> <span class="pre">*</span> <span class="pre">n</span> <span class="pre">*</span> <span class="pre">k</span></code> samples are drawn.  Default is None, in which case a
single value is returned.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">int or ndarray of ints</span></dt><dd><p><em class="xref py py-obj">size</em>-shaped array of random integers from the appropriate
distribution, or a single such random int if <em class="xref py py-obj">size</em> not provided.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.random.randint.html#numpy.random.randint" title="numpy.random.randint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randint</span></code></a></dt><dd><p>Similar to <a class="reference internal" href="#numpy.random.random_integers" title="numpy.random.random_integers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_integers</span></code></a>, only for the half-open interval [<em class="xref py py-obj">low</em>, <em class="xref py py-obj">high</em>), and 0 is the lowest value if <em class="xref py py-obj">high</em> is omitted.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>To sample from N evenly spaced floating-point numbers between a and b,
use:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">+</span> <span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_integers</span><span class="p">(</span><span class="n">N</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mf">1.</span><span class="p">)</span>
</pre></div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_integers</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go">4 # random</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">type</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_integers</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
<span class="go">&lt;class &#39;numpy.int64&#39;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_integers</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="go">array([[5, 4], # random</span>
<span class="go">       [3, 3],</span>
<span class="go">       [4, 5]])</span>
</pre></div>
</div>
<p>Choose five random numbers from the set of five evenly-spaced
numbers between 0 and 2.5, inclusive (<em>i.e.</em>, from the set
<img class="math" src="../../../_images/math/c5fcf1d7a501e476c76014f6a40ac7be7a934d79.svg" alt="{0, 5/8, 10/8, 15/8, 20/8}"/>):</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="mf">2.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_integers</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,))</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mf">4.</span>
<span class="go">array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ]) # random</span>
</pre></div>
</div>
<p>Roll two six sided dice 1000 times and sum the results:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">d1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_integers</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">d2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_integers</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dsums</span> <span class="o">=</span> <span class="n">d1</span> <span class="o">+</span> <span class="n">d2</span>
</pre></div>
</div>
<p>Display results as a histogram:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">count</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">ignored</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">dsums</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<div class="figure align-default">
<img alt="../../../_images/numpy-random-random_integers-1.png" src="../../../_images/numpy-random-random_integers-1.png" />
</div>
</dd></dl>

</div>


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